CN111062583A - Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method - Google Patents
Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method Download PDFInfo
- Publication number
- CN111062583A CN111062583A CN201911187531.5A CN201911187531A CN111062583A CN 111062583 A CN111062583 A CN 111062583A CN 201911187531 A CN201911187531 A CN 201911187531A CN 111062583 A CN111062583 A CN 111062583A
- Authority
- CN
- China
- Prior art keywords
- pci
- principal component
- index
- historical maintenance
- component analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Operations Research (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Computational Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Business, Economics & Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Tourism & Hospitality (AREA)
- Bioinformatics & Computational Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Marketing (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Road Repair (AREA)
- Road Paving Structures (AREA)
Abstract
The invention discloses a quantitative evaluation method for the historical maintenance benefit of an asphalt pavement based on a principal component analysis method, which comprises the following steps: (1) acquiring road use performance index detection data of at least one year before and after the occurrence of the historical maintenance of the road section, and performing nonlinear curve fitting on the decay of the use performance indexes PCI and RQI by using a Sun's decay model; (2) solving the decay rate of the service performance index one year after the maintenance intervention; (3) solving the decay rate of the service performance index of the corresponding year under the state without maintenance intervention; (4) solving the lifting correction value and the decay rate improvement value of the service performance index under the percent; (5) constructing a PQII evaluation system of historical maintenance on service performance improvement conditions; (6) and (4) solving each weight coefficient in a PQII evaluation system to finish index evaluation. The invention uses the historical road surface detection data to carry out decay fitting, quantifies the historical maintenance benefit, and establishes a reliable, reasonable and efficient evaluation system.
Description
Technical Field
The invention belongs to the field of road engineering, and relates to a quantitative evaluation method for historical maintenance benefits of an asphalt pavement based on a principal component analysis method.
Background
Most of the high-grade highway pavements in China are asphalt pavements. In the service process of asphalt pavement, the safety performance and the service performance of the highway are reduced due to the comprehensive circulation effect of vehicle load and natural environment. The scientific and efficient maintenance decision can play a role in delaying the development of diseases and improving road conditions.
In the existing research on asphalt pavement maintenance decision, a new process and the improvement effect of a new material on related diseases in road maintenance are mainly researched by an outdoor test section and an indoor test mode. However, for the maintenance method adopted in the historical maintenance, whether the maintenance method can be applied to the existing maintenance requirement is often determined only by empirical judgment and simple mathematical statistics. The defect of doing so is that the improvement of the road surface use performance of the relevant road section by the historical maintenance method is not specifically quantified.
The service performance evaluation system adopted in the current asphalt pavement specification in China is a pavement service performance index PQI evaluation system, and is mainly calculated by four indexes of pavement damage PCI, running quality RQI, pavement rut RDI and pavement skid resistance SRI in a linear weighted summation mode. From the perspective of the structure and the driver, the contribution of the historic maintenance method to the road maintenance can be considered as two contributions to the road damage PCI and the running quality RQI. Maintenance intervention can improve the related service performance index value in the next year and delay the decay rate of the corresponding index, so that quantitative analysis of contribution of historical maintenance to the service performance of the road can be regarded as multi-factor analysis. The principal component analysis method is a method for determining the weight of main factors in the multi-factor analysis method, and the method can well remove the cross overlapping part among each subentry index.
However, this method cannot establish a quantitative evaluation method for evaluating the effect of improving the road use performance by historical maintenance, and cannot fully utilize historical maintenance and detection data.
Therefore, the method adopts a principal component analysis method, and establishes a quantitative evaluation method for evaluating the road use performance improvement effect of historical maintenance on the aspects of road surface damage and driving quality, so that the full utilization of historical maintenance and detection data is realized, and a new cut-in angle is provided for future maintenance decision research.
Disclosure of Invention
In order to solve the technical problems, the invention provides a quantitative evaluation method for the historical maintenance benefit of the asphalt pavement based on a principal component analysis method. The method is based on two aspects of pavement damage and running quality, establishes a quantitative evaluation method for evaluating the effect of historical maintenance on improving the pavement use performance, realizes the full utilization of historical maintenance and detection data and the quantitative evaluation of historical maintenance benefits, provides a new cut-in angle for future maintenance decision research, and can realize the theoretical basis for scientifically selecting the asphalt pavement maintenance method.
The technical scheme provided by the invention is as follows:
a quantitative evaluation method for the historical maintenance benefit of an asphalt pavement based on a principal component analysis method comprises the following steps:
(1) obtaining road use performance index detection data of at least one year before and after the historical maintenance of the road section, and carrying out nonlinear curve fitting on the decay of the use performance indexes PCI and RQI by utilizing a Sun's decay model
(2) Performing first-order derivation on the fitted curve in the step (1), and solving the decay rate of service performance indexes PCI and RQI one year after the historical maintenance intervention;
(3) acquiring road use performance index detection data of at least one year before the historical maintenance of the road section, and simulating and solving the decay rate of use performance indexes PCI and RQI of one year after the historical maintenance intervention of the step (2) in a non-maintenance intervention state by using the method of the steps (1) and (2);
(4) solving the lifting correction value A, C and the decay rate improvement value B, D of the service performance indexes PCI and RQI under the percent;
(5) construction of PQII evaluation system for use performance improvement of historical maintenance
(6) And solving the weight coefficient by a principal component analysis method, and completing construction of an index system to evaluate the improvement condition of the historical maintenance on the road surface use performance.
Specifically, the fitting formula of the grand-son decay model in the step (1) is as follows:
wherein, y is PCI or RQI, and x is service life; PPI0The initial value of the service performance index when a road is newly built is generally 100; t, U is a model fitting parameter, the size of T is related to the service life of the road surface, and the size of U reflects the difference of curve shape.
Specifically, the formula of step (2) is as follows:
wherein y' is the decay rate of PCI or RQI; PPI0The initial value of the service performance index when a road is newly built is generally 100; t, U is a model fitting parameter, the size of T is related to the service life of the road surface, and the size of U reflects the difference of curve shape.
Specifically, the calculation formula of the PCI lift correction value a in step (4) is as follows:
in the formula: a is PCI lifting correction value,%; delta PCI is PCI lifting amplitude,%; delta PCImaxThe maximum amplitude can be increased for PCI;
the same method is used to calculate the RQI lifting correction value C.
Further, the Δ PCImaxThe maximum range which can be promoted is determined by the value range of PCI classification in the technical Specification of road asphalt pavement, as shown in the following table:
TABLE 1 "technical Specification for maintaining asphalt road surface" PCI classification boundary condition table
Specifically, the decay rate improvement value B in the step (4) is calculated by the following formula:
in the formula: Δ PCI' is the rate of decay of PCI after a maintenance intervention.
D was calculated in the same manner.
Specifically, the PQII evaluation system formula in step (5) is as follows:
PQII＝A·wA+B·wB+C·wC+D·wD
in the formula: PQII is road surface usability improvement index (road surface quality improved index); a, wALifting the correction value (%) and the weight coefficient thereof for the PCI; b, wBThe PCI decay rate improvement value (%) and the weight coefficient thereof; c, wCLifting the correction value (%) and the weight coefficient thereof for the RQI; d, wDThe RQI decay rate improvement (%) and its weight factor.
Specifically, the method for calculating the weighting coefficients in the steps (5) and (6) is as follows: and (3) taking the solved A, B, C, D four-index calculation value as original data, constructing a 3 x 4 original data matrix, substituting the original data matrix into the factor analysis of SPSS data analysis software, and solving each subentry index weight coefficient by a principal component analysis method.
Further, the principal component analysis method is as follows: outputting a component matrix containing initial factor load by adopting a factor analysis algorithm; converting the initial factor load into a characteristic vector value; then multiplying and summing the characteristic vector numerical value and the corresponding original data under each type of indexes to obtain each subentry index; calculating the proportion of each subentry index in the total index, and taking the value as the weight of each subentry index; and sorting the corresponding characteristic values of the components from large to small, and taking the components with the cumulative variance contribution rate of more than 80% as principal components, wherein the component with the largest variance is the first principal component, the second principal component and so on.
The invention has the beneficial effects that:
according to the method, firstly, nonlinear curve fitting is carried out on road surface service performance indexes of relevant road sections through a Sun's decay model, model parameters are solved, the service performance index decay rate under the condition of considering maintenance intervention and assuming maintenance without intervention is solved by using a first-order derivative expression of the model, and the hundred differentiation of service performance index decay rate change conditions is completed. And then, finishing the hundred differentiation of the service performance index change condition according to the rating limit value of the corresponding index of the specification. And finally, constructing a PQII evaluation system of historical maintenance on the service performance improvement condition by using four-item subentry indexes based on a principal component analysis method. The invention can quantify the benefits of the existing maintenance means, so that the highway maintenance manager can select the road combination actual situation of the existing method when developing the maintenance work, the utilization rate of the detection and maintenance data is improved, and the asphalt pavement maintenance decision is more reasonable and efficient, and the method specifically comprises the following steps:
(1) decay fitting is carried out by using historical road surface detection data, so that the result is more reliable
The evaluation method adopts continuous year pavement service performance detection data, and carries out decay fitting of corresponding indexes along with service years by using data results of PCI and RQI for many years. The calculation process is simple and easy to understand, and the data comes from engineering practice and serves the engineering practice, so that the reliability of the calculation result is high.
(2) The historical maintenance benefits are quantized, so that the maintenance decision is more reasonable and efficient
The evaluation method realizes the percentage of the itemized indexes by comparing the change conditions of the values and the decay change rates of the pavement damage index PCI and the driving quality index RQI under the condition of considering maintenance intervention and assuming maintenance without intervention, establishes a quantitative evaluation PQII evaluation system for the improvement of the service performance by historical maintenance, provides a quantitative basis for selecting the existing maintenance means and technology for maintenance workers, and improves the rationality and the high efficiency of maintenance decision.
(3) The established historical maintenance quantitative evaluation method lays a foundation for subsequent research
The evaluation method opens a new research angle on the basis of the traditional new technology and new material research and maintenance, considers the evaluation and utilization of historical maintenance and lays a foundation for the subsequent research.
Drawings
FIG. 1 is a fitting graph of Sunwer decay curves of PCI data points in three years from road section No. 1 to road section No. 1 in 2017;
FIG. 2 is a fitting graph of Sunwer decay curves of PCI data points for three years from road section No. 2 to road section No. 2 in 2017;
FIG. 3 is a fitting graph of Sunwer decay curves of three-year PCI data points in road section No. 3 from 2015 to 2017;
wherein, the year 2001, namely the year of building a universal vehicle in the north section of the high-speed lake of hong Kong, Beijing, is taken as a scale point 0 on the abscissa.
Detailed Description
In order to clarify a quantitative evaluation method for the historical maintenance benefit of asphalt pavement based on principal component analysis, the following is a typical example to further illustrate the technical solution of the present invention, but not to limit the scope of the present invention.
Examples
(1) Selecting example road segments
The example road section is located in the north section of the high-speed lake in Beijing hong Kong, and consists of two parts, namely a Beijing hong Kong Australia section (G4) and a Shang Yu section (G50), and is an important economic traffic artery in the south and north directions of China. The calculation example takes the most prominent transverse crack diseases of the riser section as an example, and selects example road sections in the table 2. The middle repair of the three road sections is carried out in 2016.
Table 2 example road segment selection results
(2) Decay curve fitting
And fitting PCI and RQI data in the road surface detection of 2015-2017 of the three road sections by adopting a SunShelter decay model to obtain model parameters considering 2016 middle repair maintenance intervention and assuming 2016 middle repair maintenance non-intervention.
For the case of considering the intervention of maintenance in 2016, curve fitting is carried out through performance indexes in three years 2015 to 2017, and model parameters are solved. The 2017 decay rate is then solved according to a first derivative model. For the case of supposing that the intermediate repair maintenance in 2016 is not intervened, in data points from 2015 to 2017, point locations in 2017 are abandoned, and the model parameters are obtained by performing Sunsliak decay model fitting on the data only in 2015 and 2016. The decay rate of 2017 years under the condition of simulating 2016 year maintenance without intervention is obtained by solving through a first derivative model. FIGS. 1-3 are fitting graphs. Calculation of the improved value of the decay rate after the maintenance intervention can then be completed. The calculation results are shown in tables 3 to 4.
PCI related index calculation results of road sections from Table 31 to No. 3%
RQI related index calculation results of No. 41 to No. 3 road sections in table%
(3) Method for solving PQII (quality of service) subentry index weight by principal component analysis method
Taking the four index calculation values of the No. 1 to No. 3 road section A, B, C, D as original data, constructing a 3 x 4 original data matrix, substituting the original data matrix into the factor analysis of SPSS data analysis software, and solving the weight coefficient of each sub index by a principal component analysis method. And sorting the corresponding characteristic values of the components from large to small, and taking the components with the cumulative variance contribution rate of more than 80% as principal components, wherein the component with the largest variance is the first principal component, the second principal component and so on. The total variance calculation results are in table 5.
TABLE 5 Total variance case
As seen from the results in Table 5, there was only one main component, which had a characteristic value of 2.916. When the SPSS carries out principal component analysis, a factor analysis algorithm is adopted, so that the numerical value l in the characteristic vector cannot be directly calculatedijBut instead outputs a component matrix containing data as the initial factor load fij. But lijAnd fijThere is a conversion relationship as shown below.
In the formula: lambda [ alpha ]jIs the eigenvalue found in the principal component analysis.
The composition matrix results are output in table 6.
TABLE 6 component matrix case
The results of solving for the values of the elements of the eigenvectors are shown in table 7.
TABLE 7 feature vector element case
Finding the eigenvector element l from the component matrixijThen, multiplying and summing the value of the partial indexes by corresponding original data of each road section under each index type to obtain each subentry index Vij. Then calculating each subentry index VijAccount for the total VijAnd the ratio of the sum is used as the index weight of each subentry. The results of the index weight calculations are shown in table 8.
TABLE 8 subentry index weight calculation
(3) Calculating PQII value of each road section and verifying the PQII value according to historical maintenance actual conditions
The results of the PQII calculation for each link are shown in table 9.
PQII calculation results for road sections 91-3
From the calculation results in table 9, it can be seen that for the road surfaces of the road segments 1 to 3 under the transverse crack damage, the improvement effect of the 2016 maintenance measure on the road surface service performance is ranked from good to bad as the road segment 3, the road segment 2 and the road segment 1.
The disease characteristics and 2016 repair and maintenance measures for road segments 1 through 3 are summarized in Table 10. The results well demonstrate that the PQII score is higher when disease characteristics are treated more specifically, thus verifying the applicability of the system.
Disease characteristics of road sections from Table 101 to Table 3 and 2016-year repair and maintenance means
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention should be included in the scope of the present invention.
Claims (9)
1. A quantitative evaluation method for the historical maintenance benefit of an asphalt pavement based on a principal component analysis method is characterized by comprising the following steps:
(1) acquiring road use performance index detection data of at least one year before and after the occurrence of the historical maintenance of the road section, and performing nonlinear curve fitting on the decay of the use performance indexes PCI and RQI by using a Sun's decay model;
(2) performing first-order derivation on the fitted curve in the step (1), and solving the decay rate of service performance indexes PCI and RQI one year after the historical maintenance intervention;
(3) acquiring road use performance index detection data of at least one year before the historical maintenance of the road section, and simulating and solving the decay rate of use performance indexes PCI and RQI of one year after the historical maintenance intervention of the step (2) in a non-maintenance intervention state by using the method of the steps (1) and (2);
(4) solving the lifting correction value A, C and the decay rate improvement value B, D of the service performance indexes PCI and RQI under the percent;
(5) constructing a PQII evaluation system of historical maintenance on service performance improvement conditions;
(6) and solving each weight coefficient in a PQII evaluation system by a principal component analysis method, and completing construction of an index system to evaluate the improvement condition of the historical maintenance on the road use performance.
2. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the fitting formula of the Sunsliaea model in the step (1) is as follows:
wherein, y is PCI or RQI, and x is service life; PPI0The initial value of the service performance index when a road is newly built is generally 100; t, U is a model fitting parameter, the size of T is related to the service life of the road surface, and the size of U reflects the difference of curve shape.
3. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the formula of the step (2) is as follows:
wherein y' is the decay rate of PCI or RQI; PPI0The initial value of the service performance index when a road is newly built is generally 100; t, U fitting parameters to the modelThe size of T is related to the service life of the road surface, and the size of U reflects the difference of curve shapes.
4. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the calculation formula of the PCI lift correction value a in the step (4) is as follows:
in the formula: a is PCI lifting correction value,%; delta PCI is PCI lifting amplitude,%; delta PCImaxThe maximum amplitude can be increased for PCI;
the same method is used to calculate the RQI lifting correction value C.
5. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 4, wherein the method comprises the following steps: the delta PCImaxThe maximum range which can be promoted is determined by the value range of PCI classification in the technical Specification of road asphalt pavement, as shown in the following table:
TABLE 1 "technical Specification for maintaining asphalt road surface" PCI classification boundary condition table
6. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1, wherein the method comprises the following steps: the decay rate improvement value B in the step (4) is calculated by the following formula:
in the formula: delta PCI' is the PCI decay rate after a maintenance intervention;
d was calculated in the same manner.
7. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method as claimed in claim 1, wherein the PQII evaluation system formula in the step (5) is as follows:
PQII＝A·wA+B·wB+C·wC+D·wD
in the formula: PQII is road surface usability improvement index (road surface quality improved index); a, wALifting the correction value (%) and the weight coefficient thereof for the PCI; b, wBThe PCI decay rate improvement value (%) and the weight coefficient thereof; c, wCLifting the correction value (%) and the weight coefficient thereof for the RQI; d, wDThe RQI decay rate improvement (%) and its weight factor.
8. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 1 or 7, wherein the method for calculating the weighting coefficient in the step (6) is as follows: and (3) taking the solved A, B, C, D four-index calculation value as original data, constructing a 3 x 4 original data matrix, substituting the original data matrix into the factor analysis of SPSS data analysis software, and solving each subentry index weight coefficient by a principal component analysis method.
9. The method for quantitatively evaluating the historical maintenance benefit of the asphalt pavement based on the principal component analysis method according to claim 8, wherein the principal component analysis method is as follows: outputting a component matrix containing initial factor load by adopting a factor analysis algorithm; converting the initial factor load into a characteristic vector value; then multiplying and summing the characteristic vector numerical value and the corresponding original data under each type of indexes to obtain each subentry index; calculating the proportion of each subentry index in the total index, and taking the value as the weight of each subentry index; and sorting the corresponding characteristic values of the components from large to small, and taking the components with the cumulative variance contribution rate of more than 80% as principal components, wherein the component with the largest variance is the first principal component, the second principal component and so on.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911187531.5A CN111062583B (en) | 2019-11-28 | 2019-11-28 | Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911187531.5A CN111062583B (en) | 2019-11-28 | 2019-11-28 | Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111062583A true CN111062583A (en) | 2020-04-24 |
| CN111062583B CN111062583B (en) | 2023-01-17 |
Family
ID=70299585
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911187531.5A Active CN111062583B (en) | 2019-11-28 | 2019-11-28 | Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111062583B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112900212A (en) * | 2021-01-21 | 2021-06-04 | 西湾智慧(广东)信息科技有限公司 | Maintenance method of dynamic maintenance mechanism based on road management maintenance |
| CN115271565A (en) * | 2022-09-29 | 2022-11-01 | 中南大学 | DEA-based method, device and equipment for evaluating highway pavement maintenance measures |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103091480A (en) * | 2013-01-07 | 2013-05-08 | 河北工业大学 | Entropy weight-based underground road bituminous pavement service performance evaluation method |
| CN104063595A (en) * | 2014-06-19 | 2014-09-24 | 交通运输部公路科学研究所 | Method for evaluating road surface usability of asphalt road |
| CN104463348A (en) * | 2014-11-11 | 2015-03-25 | 辽宁省交通科学研究院 | Modification scheme decision-making system and method for bituminous pavement |
| CN107798177A (en) * | 2017-10-13 | 2018-03-13 | 东南大学 | The optimal maintenance timing in road surface based on Pavement performance model before and after maintenance determines method |
| CN109978412A (en) * | 2019-04-10 | 2019-07-05 | 东南大学 | Choose the evaluation method of validity in a kind of asphalt pavement conserving section |
| CN110501221A (en) * | 2019-08-12 | 2019-11-26 | 武汉理工大学 | A kind of pavement performance evaluation method based on Pavement Condition and material property |
-
2019
- 2019-11-28 CN CN201911187531.5A patent/CN111062583B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103091480A (en) * | 2013-01-07 | 2013-05-08 | 河北工业大学 | Entropy weight-based underground road bituminous pavement service performance evaluation method |
| CN104063595A (en) * | 2014-06-19 | 2014-09-24 | 交通运输部公路科学研究所 | Method for evaluating road surface usability of asphalt road |
| CN104463348A (en) * | 2014-11-11 | 2015-03-25 | 辽宁省交通科学研究院 | Modification scheme decision-making system and method for bituminous pavement |
| CN107798177A (en) * | 2017-10-13 | 2018-03-13 | 东南大学 | The optimal maintenance timing in road surface based on Pavement performance model before and after maintenance determines method |
| CN109978412A (en) * | 2019-04-10 | 2019-07-05 | 东南大学 | Choose the evaluation method of validity in a kind of asphalt pavement conserving section |
| CN110501221A (en) * | 2019-08-12 | 2019-11-26 | 武汉理工大学 | A kind of pavement performance evaluation method based on Pavement Condition and material property |
Non-Patent Citations (6)
| Title |
|---|
| 李波等: "基于主成分分析法的沥青路面使用性能评价", 《长安大学学报(自然科学版)》 * |
| 杨明等: "主成分分析法在高速公路沥青路面使用性能评价中的应用", 《中南公路工程》 * |
| 许薛军等: "广东省普通国省道典型路面结构衰变规律及养护技术", 《公路交通科技》 * |
| 赵帅等: "考虑不同养护措施的路面性能衰变模型研究", 《公路》 * |
| 邓玉训等: "基于生命周期等额费用的沥青路面最佳养护时机", 《徐州工程学院学报(自然科学版)》 * |
| 高昌等: "水泥混凝土路面预养护时机选择和效果分析", 《湖南交通科技》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112900212A (en) * | 2021-01-21 | 2021-06-04 | 西湾智慧(广东)信息科技有限公司 | Maintenance method of dynamic maintenance mechanism based on road management maintenance |
| CN115271565A (en) * | 2022-09-29 | 2022-11-01 | 中南大学 | DEA-based method, device and equipment for evaluating highway pavement maintenance measures |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111062583B (en) | 2023-01-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111062583B (en) | Asphalt pavement historical maintenance benefit quantitative evaluation method based on principal component analysis method | |
| CN105469196A (en) | Comprehensive evaluation method and comprehensive evaluation system for evaluating mine construction project process | |
| CN105975711B (en) | A kind of bituminous pavement lifetime estimation method based on material damage accumulation level | |
| CN107153737B (en) | Method for determining optimal axle load period of road surface based on mechanics-experience method | |
| CN107145997A (en) | A kind of Railway Tunnel configuration state evaluation method | |
| CN109056533B (en) | Method for determining bridge-forming cable force of cable cooperation system bridge | |
| CN102680226A (en) | Method for evaluating performance of wing profile special for horizontal axis wind turbine | |
| CN107609757B (en) | Method for evaluating long-term benefit of pavement maintenance | |
| CN111724049B (en) | Research and judgment method for potential electric power energy efficiency service clients | |
| CN114004012B (en) | Optimization method for reinforced durable road test of vehicle body test yard | |
| CN106021698A (en) | Iterative updating-based UKFNN aluminum electrolysis power consumption model construction method | |
| CN115907511A (en) | Method and device for constructing adjustable load resource value evaluation model | |
| CN114529129A (en) | Low-carbon economical improvement method for urban green land planning design scheme | |
| CN111047209A (en) | Decision-making method and system for pavement preventive maintenance technology | |
| CN112989464B (en) | Method for realizing linear adjustment and cable force adjustment of integral bridge deck of suspension bridge | |
| CN110880044A (en) | Markov chain-based load prediction method | |
| CN106677079B (en) | The continuous arch bridge reinforcement means reduced based on arch bridge impost horizontal thrust | |
| CN113298409A (en) | Maintenance method based on early warning mechanism of road management and maintenance | |
| CN111626631A (en) | Evaluation method and device for power grid production technical improvement project | |
| CN116933060A (en) | Asphalt pavement targeted maintenance decision method based on rut disease cause characterization | |
| CN107563669B (en) | Highway maintenance decision analysis method based on learning model | |
| CN106021850A (en) | Entropy weight TOPSIS method based earth excavation machine combination analysis method | |
| CN111413226B (en) | Semi-rigid pavement bearing capacity evaluation method | |
| CN112950020A (en) | Asphalt pavement comprehensive performance weight determination method based on modified analytic hierarchy process | |
| CN110472877A (en) | A kind of comprehensive performance evaluation method of chain products |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |